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Multi-cloud storage can provide better features such as availability and scalability. Current works use multiple cloud storage providers with erasure coding to achieve certain benefits including fault-tolerance improving or vendor lock-in avoiding. However, these works only use the multi-cloud storage in ad-hoc ways, and none of them considers the optimization issue in general. In fact, the key to optimize the multi-cloud storage is to effectively choose providers and erasure coding parameters. Meanwhile, the data placement should satisfy system or application developers’ requirements. As developers often demand various objectives to be optimized simultaneously, such complex requirement optimization cannot be easily fulfilled by ad-hoc ways. This paper presents Triones, a systematic model to formally formulate data placement in multi-cloud storage by using erasure coding. Firstly, Triones addresses the problem of data placement optimization by applying non-linear programming and geometric space abstraction. It could satisfy complex requirements involving multi-objective optimization. Secondly, Triones can effectively balance among different objectives in optimization and is scalable to incorporate new ones. The effectiveness of the model is proved by extensive experiments on multiple cloud storage providers in the real world. For simple requirements, Triones can achieve 50 percent access latency reduction, compared with the model in $\mu$ LibCloud. For complex requirements, Triones can improve fault-tolerance level by 2 $\times$ and reduce access latency and vendor lock-in level by 30$\sim$ 70 percent and 49.85 percent respectively with about 19.19 percent more cost, compared with the model only optimizing cost in Scalia. [ABSTRACT FROM PUBLISHER]

In this paper, we present generic cloud performance models for evaluating Iaas, PaaS, SaaS, and mashup or hybrid clouds. We test clouds with real-life benchmark programs and propose some new performance metrics. Our benchmark experiments are conducted mainly on IaaS cloud platforms over scale-out and scale-up workloads. Cloud benchmarking results are analyzed with the efficiency, elasticity, QoS, productivity, and scalability of cloud performance. Five cloud benchmarks were tested on Amazon IaaS EC2 cloud: namely YCSB, CloudSuite, HiBench, BenchClouds, and TPC-W. To satisfy production services, the choice of scale-up or scale-out solutions should be made primarily by the workload patterns and resources utilization rates required. Scaling-out machine instances have much lower overhead than those experienced in scale-up experiments. However, scaling up is found more cost-effective in sustaining heavier workload. The cloud productivity is greatly attributed to system elasticity, efficiency, QoS and scalability. We find that auto-scaling is easy to implement but tends to over provision the resources. Lower resource utilization rate may result from auto-scaling, compared with using scale-out or scale-up strategies. We also demonstrate that the proposed cloud performance models are applicable to evaluate PaaS, SaaS and hybrid clouds as well. [ABSTRACT FROM AUTHOR]

Large-scale computing frameworks, either tenanted on the cloud or deployed in the high-end local cluster, have become an indispensable software infrastructure to support numerous enterprise and scientific applications. Tasks executed on these frameworks are generally classified into data-intensive and compute-intensive ones. However, most existing frameworks, led by MapReduce, are mainly suitable for data-intensive tasks. Their task schedulers assume that the proportion of data I/O reflects the task progress and state. Unfortunately, this assumption does not apply to most compute-intensive tasks. Due to biased estimation of task progress, traditional frameworks cannot timely cut off outliers and therefore largely prolong execution time when performing compute-intensive tasks. We propose a new framework designed for compute-intensive tasks. By using instrumentation and automatic instrument point selector, our framework estimates the compute-intensive task progress without resorting to data I/O. We employ a clustering method to identify outliers at runtime and perform speculative execution/aborting, speeding up task execution by up to 25%. Moreover, our improvement to bare instrumentation limits overhead within 0.1%, and the aborting-based execution only introduces 10% more average CPU usage. Low overhead and resource consumption make our framework practically usable in the production environment. [ABSTRACT FROM AUTHOR]

A virtual machine (VM) has been serving as a crucial component in cloud computing with its rich set of convenient features. The high overhead of a VM has been well addressed by hardware support such as Intel virtualization technology (VT), and by improvement in recent hypervisor implementation such as Xen, KVM, etc. However, the high demand on VM image storage remains a challenging problem. Existing systems have made efforts to reduce VM image storage consumption by means of deduplication within a storage area network (SAN) cluster. Nevertheless, an SAN cannot satisfy the increasing demand of large-scale VM hosting for cloud computing because of its cost limitation. In this paper, we propose Liquid, a scalable deduplication file system that has been particularly designed for large-scale VM deployment. Its design provides fast VM deployment with peer-to-peer (P2P) data transfer and low storage consumption by means of deduplication on VM images. It also provides a comprehensive set of storage features including instant cloning for VM images, on-demand fetching through a network, and caching with local disks by copy-on-read techniques. Experiments show that Liquid's features perform well and introduce minor performance overhead. [ABSTRACT FROM AUTHOR]

Cloud computing has received significant attention recently. Delivering quality guaranteed services in clouds is highly desired. Distributed file systems (DFSs) are the key component of any cloud-scale data processing middleware. Evaluating the performance of DFSs is accordingly very important. To avoid cost for late life cycle performance fixes and architectural redesign, providing performance analysis before the deployment of DFSs is also particularly important. In this paper, we propose a systematic and practical performance analysis framework, driven by architecture and design models for defining the structure and behavior of typical master/slave DFSs. We put forward a configuration guideline for specifications of configuration alternatives of such DFSs, and a practical approach for both qualitatively and quantitatively performance analysis of DFSs with various configuration settings in a systematic way. What distinguish our approach from others is that 1) most of existing works rely on performance measurements under a variety of workloads/strategies, comparing with other DFSs or running application programs, but our approach is based on architecture and design level models and systematically derived performance models; 2) our approach is able to both qualitatively and quantitatively evaluate the performance of DFSs; and 3) our approach not only can evaluate the overall performance of a DFS but also its components and individual steps. We demonstrate the effectiveness of our approach by evaluating Hadoop distributed file system (HDFS). A series of real-world experiments on EC2 (Amazon Elastic Compute Cloud), Tansuo and Inspur Clusters, were conducted to qualitatively evaluate the effectiveness of our approach. We also performed a set of experiments of HDFS on EC2 to quantitatively analyze the performance and limitation of the metadata server of DFSs. Results show that our approach can achieve sufficient performance analysis. Similarly, the proposed approach could be also applied to evaluate other DFSs such as MooseFS, GFS, and zFS. [ABSTRACT FROM AUTHOR]